The overall aim of this project is to increase the understanding of cardiometabolic disease by combined analysis of whole-body imaging data with other phenotype and genetic data. This by use of image analysis techniques based on both Imiomics and deep learning and applying it to data from the UK Biobank study. Specific aims are 1) To use image registration to geometrically standardize image data and create a whole-body human imaging atlas 2) To study the effect of aging on the whole-body MRI data 3) To study the association between cardiometabolic risk factors, like diabetes, hypertension, smoking, dyslipidemia, and the whole-body MRI data, controlling for effects of aging. 4) To perform genome-wide body-wide studies of body composition, to study how our genes determines differences in body composition. 5) To perform Mendelian Randomization studies of causal effects. 6) To develop new techniques for how tomographic image data can be analyzed and integrated with non-imaging data.